Chinese Journal of Lasers, Volume. 48, Issue 19, 1918007(2021)
LSTM-Based Recurrent Neural Network for Noise Suppression in fNIRS Neuroimaging: Network Design and Pilot Validation
Fig. 6. Simulation results. (a) A comparison of absorption perturbation images in CC-layer at the selected time points (colorbar: 0.0001 mm-1); (b) quantitative comparison of reconstruction at the selected time points; (c) time-courses of average absorption perturbation in the activated region and the corresponding HbO and HbR concentration perturbation
Fig. 9. In-vivo experiment results. (a) A comparison of absorption perturbation images in CC-layer at the selected time points; (b) time-courses of average absorption perturbation in the activated region and the corresponding HbO and HbR concentration perturbation
Fig. 10. Simulation results in the presence of time delay of physiological interferences. (a) A comparison of absorption perturbation images in CC-layer at the selected time points (colorbar: 0.0001 mm-1); (b) quantitative comparison of the reconstruction at the selected time points; (c) time-courses of average absorption perturbation in the activated region and the corresponding HbO and HbR concentration perturbation
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Dongyuan Liu, Yao Zhang, Yang Liu, Lu Bai, Pengrui Zhang, Feng Gao. LSTM-Based Recurrent Neural Network for Noise Suppression in fNIRS Neuroimaging: Network Design and Pilot Validation[J]. Chinese Journal of Lasers, 2021, 48(19): 1918007
Received: Feb. 2, 2021
Accepted: Mar. 29, 2021
Published Online: Sep. 24, 2021
The Author Email: Gao Feng (gaofeng@tju.edu.cn)